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remove unused INTEL_MKL_ROOT etc. #5512
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Original file line number | Diff line number | Diff line change |
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@@ -206,7 +206,7 @@ double dotProduct<double>(const int n, const double* x, const double* y) { | |
} | ||
#endif | ||
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#if defined(PADDLE_USE_MKL) || defined(PADDLE_USE_MKLML) | ||
#if defined(PADDLE_USE_MKLML) | ||
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template <> | ||
void vExp<float>(const int n, const float* a, float* r) { | ||
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@@ -295,38 +295,6 @@ template void vAdd(const int n, const double* a, const double* b, double* r); | |
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#endif | ||
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#ifdef PADDLE_USE_MKL | ||
template <> | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 这些函数不用MKL对性能的影响是什么? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 具体性能差异还没有测过,但已经发邮件希望MKLML在下一个版本发布时,加入对vInvSqrt/vLog1p/vTanh这三个函数的支持。 |
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void vInvSqrt<float>(const int n, const float* a, float* r) { | ||
vsInvSqrt(n, a, r); | ||
} | ||
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template <> | ||
void vInvSqrt<double>(const int n, const double* a, double* r) { | ||
vdInvSqrt(n, a, r); | ||
} | ||
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template <> | ||
void vLog1p<float>(const int n, const float* a, float* r) { | ||
vsLog1p(n, a, r); | ||
} | ||
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template <> | ||
void vLog1p<double>(const int n, const double* a, double* r) { | ||
vdLog1p(n, a, r); | ||
} | ||
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template <> | ||
void vTanh<float>(const int n, const float* a, float* r) { | ||
vsTanh(n, a, r); | ||
} | ||
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template <> | ||
void vTanh<double>(const int n, const double* a, double* r) { | ||
vdTanh(n, a, r); | ||
} | ||
#else | ||
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DEFINE_MATRIX_BINARY_OP(vInvSqrt, b = 1.0f / std::sqrt(a)); | ||
template <class T> | ||
void vInvSqrt(const int n, const T* a, T* r) { | ||
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@@ -357,6 +325,4 @@ template void vLog1p(const int n, const double* a, double* r); | |
template void vTanh(const int n, const float* a, float* r); | ||
template void vTanh(const int n, const double* a, double* r); | ||
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#endif | ||
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} // namespace paddle |
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这里需要知情下,下面的这些函数,MKLML里面是没有提供的,但是MKL大包里面是提供的。在MKLML时,走的是else的那一路。